Enhancing adapted physical activity training for community organizations: co-construction and evaluation of training modules
Bibliographic record
Abstract
Community-based physical activity programmes benefit persons with disabilities. However, there is a lack of evidence-based tools to support kinesiologists' training in such programmes. This study aimed to co-create and evaluate physical activity training modules for community-based adapted physical activity (APA) programmes. In Phase 1, a working group (n = 8) consisting of staff, kinesiologists from two community-based APA programmes, and researchers met over four online meetings to discuss needs, co-create training modules, and assess usability. In Phase 2, a pre-post quasi-experimental design evaluated changes in capability, opportunity, and motivation of kinesiologists (n = 14) after completing the training modules, which included standardized mock client assessments and participant ratings of module feasibility. Means and standard deviations were computed for feasibility, followed by paired-samples t-tests, along with Hedge's correction effect size. Mock client sessions underwent coding and reliability assessment. The working group meetings generated two main themes: training in (i) motivational interviewing and behaviour change techniques and (ii) optimizing APA prescription. Nine online training modules were created. In Phase 2, medium to large effects of training modules were observed in capability (Hedge's g = 0.67-1.19) for 8/9 modules, opportunity (Hedge's g = 0.77-1.38) for 9/9 modules, and motivation (Hedge's g = 0.58-1.03) for 6/9 modules. In mock client assessments, over 78% of participants appropriately used five behaviour change techniques and, on average, participants demonstrated good use of motivational interviewing strategies. The findings indicate that training kinesiologists was feasible and has the potential to enhance community-based physical activity programmes for persons with disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".